Top Senior Data Science Jobs

The Principal Data Product Scientist will oversee data needs management, product ownership, and collaboration with scientific and engineering teams to enhance analytics and data governance in Immunology R&D.

As a Senior Data Scientist, you will conduct statistical modeling, exploratory data analysis, and collaborate with product and engineering teams to integrate data science projects.

The role involves innovating and implementing advanced data science models, collaborating with engineering teams, and translating complex data into insights to drive business decisions.

Lead causal inference initiatives to measure the impact of Lyft's new product features. Collaborate with cross-functional teams to improve product evaluation through advanced measurement techniques, and mentor junior scientists in statistical modeling and experimental design.

The Senior Data Scientist will drive causal inference initiatives, develop measurement systems, mentor colleagues, and collaborate on product innovations to enhance the rider experience.

The Senior Data Scientist will develop metrics for evaluating autonomous vehicle performance, analyze data, collaborate across teams, and communicate findings to improve safety and passenger experience.

As a Senior Data Scientist, you will lead modeling initiatives, build forecasting systems, and mentor team members while collaborating with Finance, Product, and Sales teams.

The Senior Data Scientist will analyze complex datasets, develop data-driven solutions, lead teams, and leverage AI to enhance products in banking.

Lead data science work embedded in the newsroom: identify high-impact problems, build and productionize predictive and explanatory models, own core metrics/measurement, apply causal inference and experimentation, translate findings for senior stakeholders, and mentor junior data team members to improve analytic standards and adoption.

Develop and maintain simulation and optimization models for the pharmaceutical distribution network to evaluate supply/demand scenarios, transportation modes, node operations, and trade-offs (service, cost, capacity, resilience). Support tool adoption, collaborate with technical teams, and establish processes to expand and sustain analytics models.

Lead design and scaling of yield, pricing, and matching models; analyse large-scale trading and behavioral data; define data science strategy for optimisation and forecasting; partner with commercial, engineering and product teams; mentor senior/mid-level data scientists; communicate technical findings to non-technical stakeholders and ensure robust production deployment.

Lead finance-focused data science initiatives by designing, validating, deploying, and maintaining ML and Bayesian models; integrate AI tools and agents into workflows; experiment with generative AI use cases; translate technical solutions into business terms and coordinate cross-functional project execution.

The Sr. Data Scientist will utilize statistical and machine learning techniques to solve healthcare-related problems and enhance predictive modeling across products, collaborating closely with various teams.

Lead methodology and modeling for causal inference, experimentation, and predictive analytics. Design and evaluate experiments, build production-ready models and evaluation pipelines, collaborate with Product/Economics/Research/Engineering, communicate findings to varied audiences, and document institutionalized analytical practices.

Embed in a core business area to define problems, build metrics/reports, run analyses and experiments, and drive data-informed product, growth, AI, or partnership outcomes end-to-end.

Embed within a business unit to define problems, build analyses, metrics, experiments, and reports, and drive data-informed recommendations and execution across growth, product, AI/automation, or partnerships.

Lead measurement and optimization for performance marketing: design experiments, build and deploy statistical and ML models (LTV, propensity, forecasting), evolve incrementality testing, develop ETL pipelines and dashboards, translate findings into actionable recommendations, and collaborate cross-functionally to drive user growth and marketing strategy.

Embedded within a core business area, own end-to-end analytics: define problems, build metrics/reports, run analyses and experiments, surface insights, recommend actions, and drive outcomes with stakeholders.

Embed within a business unit (growth, product, AI/automation, or partnerships) to define problems, build metrics/reports, run analyses and experiments, and drive data-informed recommendations end-to-end. Execute analyses in SQL/Python, communicate findings to stakeholders, and ensure outcomes are implemented to improve key business metrics.

Design, build, and maintain data architecture and ETL pipelines to ingest, standardize, and move geospatial imagery across security domains. Manage database schemas, APIs, data health, and governance to enable NGA accreditation testing within a classified SCIF environment.

Lead data science for a new 0->1 B2B product: define strategy, frame high-impact questions, run experiments, design metrics/telemetry and dashboards, align global analytics, and translate insights into product decisions.

Lead product data scientist shaping growth strategy, experimentation, and metrics. Build self-service analytics, size opportunities, design and analyze experiments with product and engineering, drive activation/engagement/monetization insights, and evolve the company's experimentation culture.

The Sr Data Scientist will lead AI and analytics projects, mentor junior staff, and collaborate with teams to drive product innovations in the water sector.

The Senior Data Scientist will partner with multiple departments to deliver impactful data solutions, champion data adoption, and evolve data science standards.

Lead application of advanced statistical methods (SPC) across substrate factories, conduct operational audits, define and enforce quality standards (PCS, CCB, CAPA), drive continuous improvements and corrective actions, support automation roadmaps, and provide training and stakeholder guidance to scale manufacturing quality.